Data Scientist Courses, Certification & Training in Gurgaon

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Data Scientists, as the title suggests, deal with both structured and unstructured data from different sources. They are responsible for extracting useful information or knowledge from the extracted data. They usually have a variety of skills like mathematical, statistical, computing and trend-spotting. It is often dubbed as the most rewarding job in the current years.
In data science, the career path of a professional usually starts as Data Analyst, Business Intelligence Analyst, Software Engineer, or Test Analyst and with experience and skills or with the help of a certification course can become Data Scientist, Lead Data Scientist, Head of Product Analyst, and Director of Engineering.
Naukri Learning provides candidates with a variety of online course choices that would help them to become a professional data scientist. There are also a number of other related courses like Big Data Analyst, Hadoop, R, Python, Qlikview, Data Visualisation and Tableau.

A certification is like a step-up in your career and will establish your journey as an experienced data science professional.
Based on a Naukri survey, 67% of the recruiters mentioned that they prefer certified candidates and are also willing to pay higher.
Outlined below are the reasons to help you understand the advantages of a data science certification:
• Improve your current profile and resume with a certification
• With recognition, comes better salary and better job opportunities
• Your skills will be globally accepted and stand out as a certified professional
• Learn the advanced analytical techniques and skills
• Get to be proficient in the latest data analysis tools

□ After the completion of a data science training course, candidates can find opportunities in one of the professional fields immediately or in the future:
• Data Scientists
• Lead Data Scientist
• Product Analyst Manager
• Director of Engineering

Frequently Asked Questions

1. Why should I choose R programming for a data science project??

R language is useful for statistical computations, big data analysis and also for representing data graphically. In the past few years R has gained tremendous application in Big Data. Today 40% data scientists prefer R and remaining 34% prefer SAS and only about 26% go with Python. For a data science project, if you are confused between R and Python, please note that both the languages have their own pros and cons. But remember that R programming comes with advantages like Data wrangling (a way to clean messy and complex data sets so as to enable convenient consumption of data for further analysis) and all the R libraries focus on making one thing certain - to make data analysis easier. In the end it's up to you to choose it..

All major statistical software packages (like SAS, R, SPSS, Stata etc.) are similar in functionality. Some aspects may differ or may be better than another (for instance, graphics), but on the whole, most of these software packages were designed to manage and analyze data. SPSS has a powerful proprietary command syntax language. But it cannot be used to write programming codes for complex data cleaning and analyses all the time. SAS and Stata analysts are similar in functionality to SPSS as they can be used alternatively. Lastly, R is popular as it is open source as well as sophisticated..

3. How do you optimize a web crawler to run much faster, extract better information and summarize data to produce cleaner databases??

Putting a time-out threshold to less than 2 seconds, extracting no more than the first 20 KB of each pages, and not revisiting pages already crawled (that is, avoid recurrence in your algorithm) are good starting points.

4. How is machine learning different from data science??

Machine learning as well as statistical principles are a small part of data science. Algorithms applied in machine learning are data dependent and apply a training set so as to fine-tune a model for algorithmic parameters. Most of them comprise of techniques like regression, naive Bayes or supervised clustering..

5. You are about to send a million emails under a marketing campaign. How will you optimize delivery and its response using data science principles??

That is not really possible. However, a lot of data is available to marketers through website analytics, especially via Email service providers and ecommerce platforms. Adequate information can be gathered pertaining to user/consumer behavior by virtue of data science. This ranges through their preferences, choices, most preferred time, and favorite engagement medium. Data science also helps marketers to make future business predictions basing the past actions. Hence, data science is a latent tool that helps to market the most relevant products and services to a target user. Plus, it is useful to shut down abusers and spammers with the application of sophisticated AI models. This enables a spam free inbox for the user..

6. How would you turn unstructured data into structured data in data science projects??

NLP and Information Extraction are the processes to do it. Suppose you are having a template that needs to be filled with data extracted from an unstructured information feed. Honestly, this is a very basic method of creating structured data out of an unstructured feed. Based on research, you can also discover structures of data from unstructured data. While there will be no template in the same you can construct a graph with multiple nodes which in a way represent data extracts as well as links that represent how or why information that is related to each other gets fragmented..

7. What is the difference between Big Data and Data Science??

The term ‘Big Data’ is popularly used to describe exponential growth and availability of data. This data can be present in both structured and unstructured formats. Anybody who works on this data or deals with it in some way or the other to process, analyze or make sense of such massive amounts of data is a Big Data Professional. Whereas, Data Scientists are basically given the task of investigating complex problems. They apply their knowledge of mathematics and statistical principles in conjunction with computer science algorithms to arrive at answers. Such areas not only represent their knowledge but also portray that they are the most proficient Scientists of data..

8. Is it worth learning about data science??

Data Science is an ever-growing industry with a lot of scope so yes, it's worth learning. Data Scientist apart is strong business acumen, coupled with the ability to communicate findings to both business and IT leaders in a way that can influence how an organization approaches a business challenge.

9. What are the best resources to learn how to use Python for Machine Learning and Data Science??

You can learn Python through tutorials that cover concepts from beginner to advanced levels. Multiple eLearning portals provide such tutorials. You can learn Python from Books, tutorials, from MOOCs, from Paid classroom courses, from YouTube and also from live Applications. However, you cannot become a good data scientist by just learning Python. You should master Data Science with Python which covers programming with Python, Database Technologies, a good hold on Mathematics and Statistical principles as well as Machine Learning with Python. You should also be good with Information Retrieval..

10. Define data mining and data science and how are they different??

Data mining is a process used by data scientists and machine learning engineers. Data mining categorizes a family of algorithms and it is all about the process to discover data patterns. Data scientists create data products from data centric applications. It is a technical ability to handle data and scientific methods to assess its impact on a project, product or organization. Both are needed to build data products with machine learning algorithms. Such data products don’t empower business users but systems as a data scientist applies data mining processes to learn algorithms are used..

Mode of learning

The Certified Data Science Expert Program has been designed keeping in mind the requirements of the new wave of demand for strong analytics professionals. It equips you with all the conceptual and technical skills required for the ultimate position in the analytics industry. The program provides access to high quality e Learning content, simulation exams, a community moderated by experts, and other resources that ensure you follow the optimal path to your dream role of Data Scientist.

Data Science or data-driven science is that field that enables one to extract insights from data using scientific processes. Data Science Online Training course enables you to understand the practical foundations, analyse and execute Big Data to Data Analytics projects. This course also covers advanced analytic methods and tools like MapR and Hadoop. It allows students to understand the applications of these methods and tools by getting hands-on experience working alongside real time data scientists.

The Data Science Prodegree, in association with Genpact as the Knowledge Partner, is a 180-hour online program covering foundational concepts and hands-on learning of leading analytical tools, such as SAS, R, Python, Hive, Spark and Tableau through industry case studies and project work. Over the course of the 4 semesters, candidates will not only gain theoretical knowledge of key data science tools, but also gain exposure to industry best practices via guest lectures, mentorship and 6 project submissions. Genpact is a global leader in Analytics and works with over 1/5th of the Fortune Global 500 companies with revenues of $2.46 billion and 70,000 employees spread across 25 countries.

Data Science is an inter-disciplinary filed that uses scientific methods, processes and systems to extract useful insights from data. R is an open-source programming language used in data science for statistical computing and graphics. | This Certified Data Science with R Professional course has been specifically designed for candidates to get the requisite skills and knowledge to work as data scientists | It also provides the necessary training to the candidates to have working experience in R as per the requirements for data analysis and statistical computing | The course has been developed by expert professionals and has quality online learning modules | It offers candidates a course-completion certification, accepted globally

The Data Scientist Masters Program has been designed keeping in mind the requirements of the new wave of demand for strong analytics professionals. It equips you with all the conceptual and technical skills required for the ultimate position in the analytics industry. The program provides access to high quality eLearning content, simulation exams, a community moderated by experts, and other resources that ensure you follow the optimal path to your dream role of Data Scientist. | The course introduces the participant to business analytics using excel and then slowly transitions us to the most in-demand analytics technologies – R, SAS and Python– and teaches implementation of various data science concepts such as data exploration, visualization, and hypothesis testing. Special focus has been placed on predictive analytics like regression, clustering, and smoothening techniques. The entire learning experience is tied together with demos and a final project. | After completing all aspects of the training you will be well placed to take up the role of data scientist with the analytics team of your target organization.

Designed keeping in mind the needs of the new demand for strong analytics professionals, Data Scientist Masters program is equipped with all the conceptual and technical knowledge required in the analytics industry. This course also renders access to elearning content, resources, simulation exams and things that make certain the path to nurture your dream position of data scientist. Not only you gain strong analytic skill but business analytics and most in demand technologies like Python, R and SAS. Data scientist jobs are in great demand and perfect field for career.

It is necessary to have expert analytics skills to become a high-valued professional in the Analytics & KPO industry. As the industry is evolving fast, professionals need to remain up-to-date with the latest technologies while being proficient in the popular ones. If you have a comprehensive skill set, you can get a chance to work with some of the top organisations in the industry. | This Analytics & Data Science Master Subscription course provides a holistic learning approach to candidates who want to become experts in the IT & Analytics industry.
The course provides high-quality learning modules that cover major technologies and tools across multiple domains in the Analytics industry. | Some of the prominent course areas are Big data, Data Science, Machine Learning, Deep Learning, Power BI, Microsoft Excel, Data Modelling & more. | Candidates will have complete access to all the included courses for the whole course duration.

It is necessary to have expert analytics skills to become a high-valued professional in the Analytics & KPO industry. As the industry is evolving fast, professionals need to remain up-to-date with the latest technologies while being proficient in the popular ones. If you have a comprehensive skill set, you can get a chance to work with some of the top organisations in the industry. | This Analytics & Data Science Master Subscription course provides a holistic learning approach to candidates who want to become experts in the IT & Analytics industry.
The course provides high-quality learning modules that cover major technologies and tools across multiple domains in the Analytics industry. | Some of the prominent course areas are Big data, Data Science, Machine Learning, Deep Learning, Power BI, Microsoft Excel, Data Modelling & more. | Candidates will have complete access to all the included courses for the whole course duration.

This course is suitable for coding beginners because we begin with a complete introduction to coding. Then we delve deep into using pandas, an open source library with high-performance and easy-to-use data structures and data analysis tools written for Python.

In this course, we attempt to break down this complex programming language and environment into an easy to follow structured tutorial that will help you not only understand this statistical language, but also become more familiar with how you can go about using it.

This course will help you implement the methods using real data obtained from different sources.After taking this course, you will easily use packages like caret, dplyr to work with real data in R. You will also learn to use the common NLP packages to extract insights from text data.

This course is designed to get students on board with data science and make them ready to solve industry problems. This course is a perfect blend of foundations of data science, industry standards, broader understanding of machine learning and practical applications.

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